Download pdf
Citrus recognition in orchard scene based on modified HSV-morphology method
- 1 College of Computer Science and Technology (College of Date Science), Taiyuan University of Technology, Shanxi, China
- 2 College of Computer Science and Technology (College of Date Science), Taiyuan University of Technology, Shanxi, China
- 3 College of Computer Science and Technology (College of Date Science), Taiyuan University of Technology, Shanxi, China
- 4 College of Computer Science and Technology (College of Date Science), Taiyuan University of Technology, Shanxi, China
* Author to whom correspondence should be addressed.
Abstract
The orchard has always been an important scene for citrus pickers, and the existence of factors such as leaf occlusion and color similarity always lead to the difficulty of robot recognition. Based on a citrus image dataset collected from actual orchard scenes, we extract image features and build a mathematical model to identify and count the number of oranges in each image, and show the distribution of apples in the entire dataset. Firstly, this paper establishes a model based on HSV method, focusing on recognizing yellow citrus. Secondly, with the further analysis of the data set, it is found that the cyan citrus exists. Therefore, this paper defines the empirical HSV range of two kinds of citrus at the same time and introduces contour processing to optimize the model. In order to accurately distinguish the immature citrus from the leaves with similar colors and the citrus occluded by leaves, this paper uses more detailed color threshold setting and morphological operation, and makes the eccentricity of the contour less than 0.85. At the same time, considering the size and overlap of citrus, morphological operations such as dilation and erosion and contour detection techniques are applied to further improve the recognition ability of the model. Finally, the experimental results show that the average accuracy of the established model reaches 92.1%, which has a good recognition effect on citrus.
Keywords
Citrus recognition, Orchard scene, HSV method, Morphology, Contour feature
[1]. Tomas Palleja and Andrew J. Landers. Real time canopy density estimation using ultrasonic envelope signals in the orchard and vineyard[J]. Computers and Electronics in Agriculture, 2015, 115: 108-117.
[2]. Dairath Meer Hannan et al. Computer vision-based prototype robotic picking cum grading system for fruits[J]. Smart Agricultural Technology, 2023, 4
[3]. Rajasekar L. et al. Performance Analysis of Fruits Classification System Using Deep Learning Techniques[M]. Springer Singapore, 2020: 293-301.
[4]. Diego Escobar Figueroa and Edgar Roa Guerrero. Artificial vision system for the identification of ripeness of pasion fruit (granadilla)[J]. Redes de Ingeniería, 2016, 7(1): 78-86.
[5]. Saini Ashok Kumar and Bhatnagar Roheet and Srivastava Devesh Kumar. Computer Vision-Based Model for Classification of Citrus Fruits Diseases with Pertinent Image Preprocessing Method[M]. Springer Nature Singapore, 2024: 275-285.
Cite this article
Yan,J.;Liang,K.;Liu,C.;Gao,M. (2024). Citrus recognition in orchard scene based on modified HSV-morphology method. Applied and Computational Engineering,88,64-71.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 6th International Conference on Computing and Data Science
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).